Computerized systems and methods for stability-theoretic prediction and prevention of sudden cardiac death

ABSTRACT

Systems, methods and computer-readable media are provided for automatic identification of patients according to near-term risk of ventricular arrhythmias and sudden cardiac death (SCD). Embodiments of the invention are directed to event prediction, risk stratification, and optimization of the assessment, communication, and decision-making to prevent SCD, and in one embodiment take the form of a platform for wearable, mobile, unteathered monitoring devices with embedded decision support. Thus embodiments relate to automatically identifying persons at risk for arrhythmias and SCD through the use of noninvasive, portable, wearable electronic device and sensors equipped with signal-processing software and statistical predictive algorithms that calculate stability-theoretic measures derived from the digital electrocardiogram timeseries acquired by the device. The measurements and predictive algorithms embedded within the device provide for unsupervised use in the home or in general acute-care and chronic-care venues and afford a degree of robustness against variations in individual anatomy and sensor placement.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority of U.S. ProvisionalApplication No. 61/291,657, filed Dec. 31, 2009, which is expresslyincorporated by reference herein in its entirety.

BACKGROUND

Sudden cardiac death (SCD) is responsible for the death of up to 450,000persons in the U.S. each year; similar incidences of SCD occur in otherWestern nations. The majority of these cases involve ventriculararrhythmias, not coronary occlusions and myocardial infarctions. SCDpredominantly affects individuals in the prime of their lives, with mostoccurrences of life-threatening arrhythmia cases happening in thecommunity (outside the hospital). Resuscitation is only attempted in aminority of patients, in part due to unavailability of defibrillationequipment and lack of knowledge and action by lay responders.

SCD is associated with common cardiac diseases, most notably heartfailure, in which approximately 50% of patients die from fatal cardiacarrhythmias. Multiple factors in addition to reduced ejection fraction(EF) have been demonstrated to contribute to the risk for SCD aftermyocardial infarction. These include the presence of nonsustainedventricular tachycardia (NSVT), symptomatic heart failure (HF), andsustained monomorphic VT inducible by electrophysiologic cardiactesting.

However, fatal ventricular arrhythmias and SCD also frequently occur inyoung, otherwise healthy individuals without known structural heartdisease. In structurally normal hearts the most common mechanism forinduction and maintenance of ventricular tachycardia is abnormalautomaticity. One form of abnormal automaticity, known as ‘triggeredarrhythmias,’ is associated with aberrant release of Ca⁺² that initiatesdelayed after-depolarizations (DADs). DADs, which can trigger fatalventricular arrhythmias, are abnormal depolarizations in myocardialcells that occur after repolarization of a cardiac action potential. Themolecular basis for abnormal Ca⁺² release that causes DADs is, to date,incompletely understood.

In another form of abnormal automaticity in persons with an inherited,arrhythmogenic disorder known as “catecholaminergic polymorphicventricular tachycardia”, physical exertion and emotional stress inducepolymorphic ventricular tachycardias that lead to SCD in the absence ofdetectable structural heart disease.

On the cellular level, prolonged repolarization can result in earlyafter-depolarizations (EADs), which are also arrhythmogenic. Predictionof SCD based on increased beat-to-beat T-wave lability duringcatecholamine-provocation has been examined previously.

Depending on the arrhythmogenic mechanism(s) that prevail at a giventime for a particular individual, specific steps to prevent SCD can beselected and taken, notably for people who are in hospital at the timewhen a ventricular arrhythmia occurs. However, comprehensive preventionis hampered by multiple factors. Foremost among these is the presentinability to identify predictive factors for the majority of patients atrisk of SCD and to do so far enough in advance of the event thatassessment and prevention/treatment services can be effectivelyimplemented. The reason why this is so is that many who are at risk ofSCD have no prior evidence of cardiac disease and are therefore notcurrently engaged with a health care system where assessment andprevention might take place.

Furthermore, even in patients at markedly elevated risk, amiodarone andother conventional, nonspecific anti-arrhythmic drug treatments haveproven largely ineffective in preventing SCD particularly for ambulatorypatients when the SCD event occurs outside of a hospital. Antiarrhythmicdrugs have failed to prevent SCD in healthcare venues (ambulatory oracute care) owing substantially to the lack of timely dosing anddose-range adjustment of the medication in advance of the arrhythmiaevent; or to poor effectiveness on account of the nonspecificity or lackof relation of the prevailing arrhythmogenic mechanism and the selecteddrug's mechanism of action; or to adverse effects associated with themedication selected. Notwithstanding the historical reasons for lack ofeffectiveness to-date, were advance warnings and timely dosingimplemented, the existing medications and new ones that are now underdevelopment may have a better chance of reducing SCD rates and improvingsurvival.

Additionally, guidelines for medical device-based preventive therapies,such as implantable cardioverter-defibrillators (ICDs) or cardiacresynchronization therapy (CRT) for patients at elevated risk, are oftennot followed. In the case of ICDs, the reason is partly becausephysicians perceive that the majority of patients who receive theseexpensive, invasive therapies never experience life-threateningarrhythmias that would cause the implanted device to delivercardioversion-defibrillation discharges.

It is because of these factors that an improved predictive-preventivemethod and system would be valuable, and in embodiments of such methodsand systems, prediction classification or decision-support alert signalsemitted by the system may be provided at logistically convenient timesfar enough in advance of a life-threatening arrhythmia's occurrence toallow for effective preventive intervention in a majority of cases. Moreover, embodiments of such a method and system can be inexpensive andsuitable for a much larger population who are at moderate risk of SCD.Such a system could find use as a tool not only for surveillance andtriaging the general medical-surgical patients in hospitals and otheracute-care venues but also for ambulatory, free-living individuals suchas athletes and the general elderly population who have one or morerisk-factors for SCD.

Effective SCD preventive interventions vary and optimal selection andpersonalized tailoring of them can depend upon the patient's context,gender, age, heart conditions such as heart failure or coronary arterydisease or left ventricular hypertrophy, ejection fraction, exerciseinducibility of ventricular tachycardia, comorbid illnesses, concomitantmedications, electrolyte abnormalities, family history of SCD, and otherfactors. In the case of a previously asymptomatic ambulatory person,effective preventive interventions may include consultation with thepersonal physician, presentation at a nearby emergency department fordiagnostic assessment and close monitoring, and, optionally, prophylaxiswith amiodarone or ranolazine loading or, in some situations, azimilide,dofetilide, or sotalol. In the case of a person with existing, knowncardiac conditions, effective preventive interventions may includeadmission to hospital for observation and cardiac electrophysiologyexams, provision of external pacing and resuscitation equipment at theready, consideration for implantation of an ICD, or other alternatives.

Conventional cardiac rhythm measurements, such as R-R dispersion orabnormal QTc or QT dispersion (QTd), based on small samples of ECGwaveforms acquired over short intervals (10 to 30 sec) have been shownto have inadequate statistical sensitivity and specificity for thepurpose of predicting SCD.

When measurements rely upon apnea or disturbed respiratory patterns asthe trigger or sentinel event for predicting incipient cardiacarrhythmias, the predictions are generally only relevant when the personis asleep. Additionally, the advance notice provided by disturbedrespiratory pattern signals is so short (tens of seconds) as to precludeeffective interventions to prevent the predicted arrhythmia or SCDoccurrences.

Many prior art methods involve cumbersome, complex, expensive and/orinvasive instrumentation, or require a skilled operator in attendance.

The most accurate predictive methods, such as paced electrogramfractionation analysis (PEFA), are highly invasive (involve placement ofmultiple catheters in the heart), are expensive, are not widelyavailable, are only performable by subspecialty-trained cardiologists,and are only applicable to a small subset of patients who are alreadyknown to be at risk of SCD based on other attributes.

The methods involve expensive measurements, such as genomic or proteomiclaboratory tests that are not widely available and that have aperformance turnaround time of many hours or days before the results andprediction are available for use, such that the prediction orclassification is not timely with respect to interventions aimed atpreventing the predicted occurrences.

The methods are sensitive to, and may be compromised or entirelyconfounded by, individual variations in patient anatomy and physiology,such as cardiac axis deviation, pulmonary congestion, dyspnea, skeletalmuscle signal artifact, patient movement and positioning, diurnalvariations, etc.

The methods are sensitive to, and may be compromised or entirelyconfounded by, individual variations in operator positioning ofelectrodes or sensors on the patient's body or variations in the timingand method of acquiring the specimens or data that will enter into theprediction and classification.

Noninvasive electrocardiographic tools that have been approved by theU.S. Food and Drug Administration for identifying patients at risk forSCD (such as signal-averaged electrocardiogram (SAECG) and T-wavealternans (TWA) analysis) are relatively time-consuming to perform and,as such, are accessible to only a small subset of persons at-risk,mostly less healthy persons in acute-care settings, and even in thispopulation exhibit a false-negative rate of more than 50%, in partbecause the interval of data capture is limited to the time of the exam.

QT interval dispersion (QTd) is still the most common andgenerally-available measure used to detect repolarization problems, butthis too is generally only measured in a per-exam, discrete, “snapshot”fashion. While QTd is routinely measured using manual ECG methods,software algorithms to automatically perform the measurement areavailable, and these could be implemented in a continuous fashioninstead of discrete, point-in-time snapshots. However, these prior artsoftware algorithms suffer from the same signal-processing problems andartifacts that arise in measuring the QT interval generally. The mostcommon alternative approach has been to devise measurements thatconsider the T-wave's morphology, or overall shape. For example, oneapproach used the width of root-mean square curves of T-waves and foundmuch higher correlation with the repolarization dispersion than wasfound for QTd. Another approach compared several novel computationalmeasures of T-wave morphology with QTd. But short-term measurement ofQTd was found to be an inadequate predictor of SCD. That approach foundthat a measure named total cosine R-to-T T-wave morphology dispersionwas useful in assessing malignant arrhythmia risk in post-myocardialinfarction patients. Machine learning techniques have also been appliedto various aspects of the repolarization-dispersion problem. Oneapproach used principal component analysis (PCA) to detectrepolarization abnormalities and found the method outperformed QTd formen, and exhibited predictive performance equivalent to QTd for women.Wavelet analysis, Gaussian mesa function analysis, and machine learningapproaches have been used for ECG delineation, QT interval measurement,and rhythm classification. Machine learning has also been applied topredict the occurrence of drug-induced Torsades de Pointes.

But the prior art is deficient of teachings regarding examiningmathematical stability properties of the measured variables. Nor has theprior art made use of continuous realtime measurements over long periodsof many hours, for instance. Despite the existence of Holter monitortype ECG recording equipment for approximately 40 years, the analysis oflong-timeseries Holter data is traditionally restricted to abnormalbeats or rhythms, and calculation and study of RRd(t), QTd(t), and otherparameters are never performed. Only small selected portions of therecorded data are subjected to detailed analysis, and the rest aretypically discarded unexamined or ignored.

SUMMARY

A system, methods and computer-readable media are provided for theautomatic classification of patients according to near-term risk oflife-threatening ventricular arrhythmias and sudden cardiac death.Embodiments of the invention are directed to event prediction, riskstratification, and optimization of the assessment, communication, anddecision-making to prevent sudden cardiac death in humans.

In embodiments, a method for automatically predicting ventriculararrhythmias in an individual that are likely to result in sudden cardiacdeath (SCD) is provided. The method includes the step of obtaining ECGsignals representative of electrical activity of the heart of anindividual. The method also includes the steps of determining, utilizingan objective function, a QT dispersion stability index (QTdSI) from thesignals, and determining the difference between the index and areference value to detect the presence of instability of QT intervaldispersion or other measurements in said signals, wherein a significantdifference is indicative of an increased risk of said individual of SCD.In one embodiment, the objective function comprises a timeseriescalculated from serially-acquired waveform data embodying a Lyapunovexponent of one or a plurality of ECG or other physiologic variables asfunctions of time. In one embodiment, the method further includesproviding a notification when an increased risk for SCD is determined.In some embodiments, this notification may me communicated to a healthcare provider and/or may be communicated to the individual by means ofan audible alarm, text message, or phone call.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is described in detail below with reference to theattached drawing figures, wherein:

FIG. 1A depicts aspects of an illustrative operating environmentsuitable for practicing an embodiment of the invention.

FIG. 1B depicts aspects of an illustrative operating environmentsuitable for practicing an embodiment of the invention.

FIG. 2 depicts aspects of an illustrative operating environment suitablefor practicing an embodiment of the invention.

FIG. 3 depicts a flow diagram of an exemplary method for automaticallypredicting ventricular arrhythmias in an individual that are likely toresult in sudden cardiac death, in accordance with embodiments of theinvention;

FIG. 4 depicts a flow diagram of an exemplary method for determining aQT dispersion stability index for an individual, in accordance withembodiments of the invention;

FIG. 5 depicts a flow diagram of an exemplary method for determining aQT dispersion stability index for an individual, in accordance withembodiments of the invention;

FIG. 6 depicts an exemplary EKG signal of normal morphology identifyingthe P, Q, R, S, and T segments of the waveform.

FIG. 7 illustratively depicts inter-related causative factors resultingin SCD.

DETAILED DESCRIPTION

The subject matter of the present invention is described withspecificity herein to meet statutory requirements. However, thedescription itself is not intended to limit the scope of this patent.Rather, the inventors have contemplated that the claimed subject mattermight also be embodied in other ways, to include different steps orcombinations of steps similar to the ones described in this document, inconjunction with other present or future technologies. Moreover,although the terms “step” and/or “block” may be used herein to connotedifferent elements of methods employed, the terms should not beinterpreted as implying any particular order among or between varioussteps herein disclosed unless and except when the order of individualsteps is explicitly described.

As one skilled in the art will appreciate, embodiments of our inventionmay be embodied as, among other things: a method, system, or set ofinstructions embodied on one or more computer readable media.Accordingly, the embodiments may take the form of a hardware embodiment,a software embodiment, or an embodiment combining software and hardware.In one embodiment, the invention takes the form of a computer-programproduct that includes computer-usable instructions embodied on one ormore computer readable media.

Computer-readable media include both volatile and nonvolatile media,removable and nonremovable media, and contemplates media readable by adatabase, a switch, and various other network devices. By way ofexample, and not limitation, computer-readable media comprise mediaimplemented in any method or technology for storing information.Examples of stored information include computer-useable instructions,data structures, program modules, and other data representations. Mediaexamples include, but are not limited to information-delivery media,RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM,digital versatile discs (DVD), holographic media or other optical discstorage, magnetic cassettes, magnetic tape, magnetic disk storage, andother magnetic storage devices. These technologies can store datamomentarily, temporarily, or permanently.

Embodiments of the present invention provide a computerized system,methods, and computer-readable media for automatically identifyingpersons who are at risk for cardiac arrhythmias and sudden cardiac deaththrough the use of a system, which in one embodiment, includesnoninvasive, portable, wearable electronic device and sensors equippedwith signal-processing software and statistical predictive algorithmsthat calculate stability-theoretic measures derived from the digitalelectrocardiogram timeseries acquired by the device. FIG. 7illustratively depicts inter-related causative factors resulting in SCD.

The measurements and predictive algorithms embedded within the deviceprovide for unsupervised use in the home or in general acute-care andchronic-care venues and afford a degree of robustness against variationsin individual anatomy and sensor placement. In embodiments, the presentinvention provides a leading indicator of near-term futureabnormalities, proactively alerting the user (for example, 2 hours ormore in advance, in one embodiment) and providing the wearer and/or careproviders with sufficient advance notice to enable effective preventivemaneuvers to be undertaken. In one exemplary embodiment, the device isequipped with radiofrequency telecommunication capabilities that enableintegration with case-management software, electronic health recorddecision-support systems, and consumer personal health record systems.

By way of example and not limitation, a user using an embodiment of theinvention may be able to go about his or her daily routine but beprovided an advanced warning of any abnormalities such as a detonationor improvement of the user's condition or an increased likelihood of anevent such as SCD, COPD, asthema, TIA, stroke, or other conditions, forexample. In one embodiment, the user may don one or more sensors, whichcould be a chest-strap sensor, a badge sensor attached to or integratedinto the user's clothing, a watch-sensor or other sensor in approximatecontact with the user and that is wirelessly communicatively-coupled toa smart phone located on or near the user's body. In this exemplaryembodiment, the smart-phone may include an app which when executedreceives user data from the sensors, calculates the stability-theoreticmeasures, and communicates the results with the user, the user's healthcare provider, case-management software, decision-support systems, orpersonal health record systems. For example, the phone may notify theuser in advance, via an alarm or vibration, and may also notify a familymember, the user's health care provider, electronic-health recorddecision-support systems or personal health record systems, via a call,email, http, sms text-message, or other form of radiofrequencycommunication, that the user has an increased likelihood of a near-termfuture abnormality. This enables the user or care providers to takepreventative measures.

An exemplary operating environment for the present invention isdescribed in connection to FIGS. 1A, 1B and 2, and relates generally tothe description of a mobile wearable system for stability-theoreticprediction and prevention of events such as SCD, for use in someembodiments of the invention, and described below in connection to FIGS.1A, 1B and 2. Referring to the drawings in general, and initially toFIG. 1A in particular, an exemplary operating environment 100 isprovided suitable for practicing an embodiment of our invention. We showcertain items in block-diagram form more for being able to referencesomething consistent with the nature of a patent than to imply that acertain component is or is not part of a certain device. Similarly,although some items are depicted in the singular form, plural items arecontemplated as well (e.g., what is shown as one data store might reallybe multiple data-stores distributed across multiple locations). Butshowing every variation of each item might obscure the invention. Thusfor readability, we show and reference items in the singular (whilefully contemplating, where applicable, the plural).

As shown in FIG. 1A, environment 100 includes one or more sensors 116.In one embodiment, sensors 116 include one or more transducers or typesof sensors operable for providing electrical signals corresponding tomeasurements of various conditions or states of a user. Embodiments ofsensor 116 may further include a power supply, processor, memoryoperable for acquiring and storing user-information and programminginstructions, and communication component for communicating theresulting measurements of user-information with brick 130. In someembodiments, the transducer may be a standard electrode, such as asingle-terminal electrode, or a specialized multi-segment ornoise-reduction electrode.

In some embodiments one or more specialized noise-reduction electrodesmay be integrated on a wearable fabric elastomeric band positioned onthe user, such as around the user's chest, thereby eliminating orreducing noise, interference, distortion, or artifacts and alsoimproving ease-of-use and patient compliance. In some embodiments, theprocessor of sensor 116 is operable to control the frequency ofmeasurements; for example, to read a transducer's output at certainintervals such as 50 times each second; to pre-process or condition thesignal, including applying a threshold, noise-filter, or normalizing theraw user-derived signal; read from or store the user-information inmemory, and communicate the acquired timeseries of user-information withbrick 130 via a communication component of sensor 116.

Embodiments of sensor 116 may be designed to measure one or moreconditions or states of a user. For example, in one embodiment sensor116 obtains electrical cardiac signals of a user and may be worn as achest-strap. Such a sensor may be designed to measure electrical signalsassociated with the nerves of the heart or the heart muscle or both. Inanother embodiment, sensor 116 may include an optical transducer formeasuring chemicals in the skin such as keytones, which may be used fordetermining ketoacidosis of the user. Such an embodiment of sensor 116may be configured as a skin patch, arm- or leg-band, on the back of awatch, or ankle band, for example. Another embodiment of sensor 116includes one or more optical sensors for detecting an optical signalacross the skin to look at carbox-symmetry, CO2 levels, O2 levels, or acombination of these levels.

In some embodiments, these levels are measured at 10 to 50 times asecond thereby resulting in a timeseries of user-information that maybecommunicated to brick 130. Other embodiments of sensors 116 includesensors for measuring blood pressure, heart rate, temperature, chemicalssuch as chemicals in the blood, breath, or on the user's skin, skin ortissue properties, oxygen levels, user motion, movement, or position, orother variables associated with the user's condition or state. Suchsensors are configured to be positioned on or near the user's body in anappropriate manner so that they may function to sense user-data. Forexample, heart-related sensors may be positioned on or near the chest orat other appropriate locations on the user's body.

In some embodiments, sensor 116 may be worn in contact with user, wornon user's clothes, or located in a user's seat, bed, toilet, orelsewhere in the user's environment, depending on specific type ofuser-information that the sensor is intended to measure. In oneembodiment, sensors 116 include one or more accelerometers, gyroscopicmeters, or combination of such devices as to enable one or more sensors116 to detect user motion, user position or orientation, and suddenchanges in user position. In one embodiment, such a sensor 116 may beoptimally positioned on the user to measure motion and orientation, suchas inline with the user's spine. In one embodiment, the accelerometerand gyroscopic chip-sets built into many smart phones may be used assensor 116. In such an embodiment, the smart phone, running a programfor determining stability-theoretic measures, may monitor usermotion-stability and provide to the user and health-care provider earlyearning warning of a likelihood of increased risk for falling.

In some embodiments, multiple sensors 116 may be employed on or aboutthe user. For example, it may be desirable to have more than one sensorfor measuring certain user information such as ketones in the skin, forexample, as circulation on certain users varies in the user's body.Additionally, one or more sensors may become compromised, and havingmultiple sensors provides for robustness. For example a watch sensor mayget wet when the user washes his hands and fail to operate as normal,while a second sensor located on the user's ankle may remain effective.It is also contemplated that multiple sensors of different sensor-typesmay be utilized to provide a combination of user-information that maymore accurately identify a condition or state of the user or increasedlikelihood of a particular event occurring. For example, a usersuffering from the early conditions of a stroke may exhibit multiplesigns detectible by different types of sensors 116, such as motionsensors 116, blood-pressure sensors 116, and skin-chemical sensors 116.

Continuing with FIG. 1A, environment 100 includesprocessing/communication brick 130. Exemplary embodiments of brick 130are discussed in greater detail in connection to FIG. 1B, but someembodiments of brick 130 include one or more processors operable forprocessing user-sensor information and determining stability-theoreticmeasures, a communication module for receiving information from theuser-sensors and for communicating results to the user or health-careprovider, and a memory for storing received user-information, determinedresults, and programming instructions. Brick 130 may worn on the user'sbody, such as clipped to a belt, in a holster, or around the user'sneck, or can be carried by the user, such as in the user's pocket orpurse, or may be kept with a close enough proximity to the user as tocommunicate with sensor(s) 116. In some embodiments, sensor(s) 116 arehoused within or on brick 130.

In some embodiments, brick 130 is a smart phone running one or moreapplication programs or “apps” for receiving user-sensor information,determining stability-theoretic measures, and communicating results tothe user and health care provider. In a smart-phone embodiment, brick130 uses the phone's communication equipment for communicating userinformation to a backend, such as a health care provider ordecision-support knowledge agent. Brick 130 may use other communicationfeatures of the smart phone such as Bluetooth or Wi-Fi to communicatewith one or more sensors 116 and in some embodiments, a base station oruser computer.

A smart phone may be communicatively-coupled with an additionalcomponent for facilitating communication with one or more sensors 116,for processing user-information, or for storing and communicating userresults. For example, in one embodiment, brick 130 iscommunicatively-coupled to a holster or other component containing acommunication module for communicating with one or more sensors 116.Such an embodiment is useful where sensors 116 use a communicationprotocol that is not compatible with brick 130. For example, wheresensors communicate using Bluetooth, but brick 130 is embodied onnon-Bluetooth enabled smart phone, the user may attach a Bluetoothmodule to the smart phone to enable it to communicate with sensors 116.Similarly, where sensors 116 communicate using ZigBee or anotherlow-rate wireless personal area network platform, a user may couple aZigBee-enabled communication module to their smart phone. In anotherexample embodiment, a smart phone may be communicatively-coupled with abase station (not shown) located in the user's house. In one embodiment,the base station could be a personal computer connected to a wirelessrouter or a laptop equipped with RF communication capability such asWi-Fi or Bluetooth. In one embodiment, the base station communicateswith backend 190.

In another embodiment, brick 130 communicates directly with backend 190.Backend 190 includes the health care provider computer system anddevices, case-management software, electronic health recorddecision-support systems and devices, or consumer personal health recordsystems and devices. In some embodiments, brick 130 stores informationon data store 192, which may be local or remotely located, and which maybe accessible by backend 190, in some embodiments. In some embodiments,data stores 192 comprises networked storage or distributed storageincluding storage on servers located in the cloud. Thus, it iscontemplated that for some embodiments, the information stored in datastore 192 is not stored in the same physical location. For example, inone embodiment, one part of data store 110 includes one or more USBthumb drives or similar portable data storage media. Additionally,information stored in data store 192 can be searched, queried, analyzedvia backend 190, such as by a health care provider or by adecision-support knowledge agent, for example.

In some embodiments, sensors 116 communicate with other sensors 116 andwith brick 130 over a wired or wireless communication protocol. In oneembodiment, sensors 116 communicate using Bluetooth, Wi-Fi, or Zigbeeprotocols. In some embodiments a low-powered communication protocol isdesirable in order to preserve the batter life of the sensor 116. Insome embodiments using a communication protocol having a narrowbandwidth, such as Zigbee, sensors 116 may also include a memory bufferfor storing user-derived information until it is communicated to brick130. Sensors 116 may also communicate with other sensors 116 or directlywith a base station, in some embodiments.

Turning now to FIG. 1B, an exemplary operating environment suitable forpracticing an embodiment of the invention is shown and referencedgenerally as 150. As shown in FIG. 1B, brick 130 is communicativelycoupled to wearable chest strap sensor 112, which is one embodiment ofsensor 116, and docking station 120. In the embodiment shown in FIG. 1B,docking station 120 recharges a battery in brick 130 and in chest-strapsensor 112. Brick 130 is also communicatively coupled to backend 190,and data store 192, which are described previously in connection to FIG.1A.

The embodiment illustratively depicted in FIG. 1B, may be used forgenerating a Lyapunov exponent classifier and verifying and validatingwhether such a detector achieves statistical sensitivity and specificityin the intended mortality range of deployment, sufficient forsatisfactory performance in the use for classifying patients accordingto in-hospital mortality outcome.

In the embodiment shown in FIG. 1B, chest-strap sensor 112 includes oneor more skin surface electrodes in the fabric of the chest strap. Inthis embodiment, the electrodes are coupled to an instrumentationoperational amplifier, an analog filter, an analog-to-digital converter,and a Bluetooth or similar RF communication component, thereby enablingchest strap sensor 112, when positioned on the user's chest, to obtainraw electrocardiogram signals of the user's heart, capture and digitizethe raw ECG signals, and communicate this information to brick 130.Chest-strap sensor 112 also includes a power supply made up of a batteryand multiple-output supply converter.

In the embodiment shown in FIG. 1B, brick 130 includes a Bluetooth orsimilar RF communication component operable to receive user-informationfrom chest-strap sensor 112 or from other sensors 116, preprocessing andfiltering components operable to condition and format the received userinformation for the QT variability index (QTVI) stability processing,and one or more processors for determining QTdSI, which is described inconnection to FIGS. 3 and 4, below. Embodiments of brick 130 may alsoinclude a Bluetooth, cell-phone, or Wi-Fi communication component forcommunicating results ultimately to backend 190 and data store 192, andan alarm and display for providing results, diagnostic feedback, powerlevels, and other information to a user or for receiving inputs from auser such as parameters and device settings. Embodiments of brick 130may also include memory for storing parameters, settings, firmware andprogramming instructions, and determined results. Embodiments of brick130 may also include a power supply which in one embodiment comprises abattery and a battery balance circuit. In one embodiment, brick 130 is acomputer system with one or more processors, memory, and input/outputfunctionality.

In one embodiment, brick 130 is a computer system comprising thefollowing hardware and firmware components: a 32-bit 48 MHz AT91SAM7S256(ARM7TDMI) main microprocessor with 256 KB flash memory and 64 KB RAM,an 8-bit 4 MHz ATmega48 microcontroller with 4 KB flash memory and 512Bytes RAM, a 26 MHz CSR BlueCore 4 Bluetooth controller with 1 MB flashmemory and 47 KB RAM, and 100×64 pixel LCD matrix display. In oneembodiment, ECG pre-processing, recursive IIR low-pass Bessel filter,and QTdSI calculation software algorithms were implemented in a dialectof the C language (NXC) using the BricxCC compiler and version 1.28firmware for the ARM7 processor. It should be understood that variationsin hardware and firmware are contemplated by and within the scope of theinvention, and are provide here for illustrative purposes.

FIG. 2 illustratively depicts aspects of an illustrative operatingenvironment suitable for practicing embodiments of the invention and isreferenced generally as 200. Environment 200 depicts a user 210 wearingvarious example types of sensors 116, including: chest-strap sensor 212,badge-sensor 214, which may be attached to a user's clothing orintegrated into a user's clothing, necklace sensor 216, skin-patchsensor 218, watch-strap sensor 220, and ankle or leg sensor 222. User210 is also wearing a brick 230 at the user's waist. Also depicted inenvironment 200 is a chair 205 having sensors 116 integrated into a seatcushion, shown as sensors 225, and a bed 207 having sensors 116integrated into the bed shown as sensors 227. In some embodiments,environment 200 includes a base station 240, which may becommunicatively coupled to brick 230 or one or more sensors 116. Asfurther described in connection to FIG. 1A, in some embodiments, a basestation, such as base station 240, is communicatively coupled to auser's computer, to a backend 190, or to data store 192.

Turning now to FIG. 5, a flow diagram 500 is provided illustrating anexemplary method according to one embodiment. At a high level, flowdiagram 500 illustratively depicts a method for determining a QTdispersion stability index (QTdSI) for an individual. An example of theQT interval is illustratively provided in FIG. 6. The QTdSI isdetermined by applying an objective function to user-derived informationsuch as ECG signal information obtained from one or more sensors 116.Some embodiments of the invention process the information in serialheartbeats in the individual's electrical cardiac signals to calculateQTdSI(t) timeseries, where t represents time, as a function of theindividual's instantaneous QTdSI determinations. As shown in flowdiagram 500, a logistic regression equation and algorithm based onLyapunov stability measures of QT dispersion as a continuous or discretefunction of time is utilized. A Lyapunov exponent (of QTdSI(t) or anyother timeseries signal) is a quantitative measure of separation oftrajectories that diverge widely from their initial positions and isrelated to how chaotic a system is. The larger the exponent, the morechaotic the system. For periodic signals, the Lyapunov exponent is zero.A random but stable signal will also have an exponent very close tozero.

In another embodiment, a decision tree algorithm may be used to evaluatethe classification ability of several methods of measuringrepolarization dispersion. In yet another embodiment, a support vectormachine (SVM) algorithm utilizing timeseries of calculated ECG variablesincluding width of root-mean-square (RMS) T-wave, total cosine T-wavedispersion, T-wave loop dispersion, normalized T-wave loop area, andrelative T-wave residuum is applied to generate a prediction of SCDrisk. Still in yet another embodiment, a combination of a Lyapunov-basedalgorithm, a decision tree algorithm, or a support vector machine may beutilized.

At a step 510, electrocardiogram (ECG) signals of a user are obtainedusing one or more sensors 116. User-information representative of theECG signals is communicated from one or more sensors 116 to brick 130.In one embodiment, the obtained ECG-signal information includes one ormore QT intervals. An example QT interval is illustratively provided inFIG. 6. In one embodiment, sensor 116 captures QT waveformscorresponding to the user's heart beats, thereby resulting in atimeseries of QT intervals. It will be understood by those skilled inthe art that in some embodiments, other ECG waveform measures orphysiologic timeseries may be used without departing from the scope ofthe invention. For example, in some embodiments timeseries variablesrelating to respiratory, glucometry, accelerometry, oximetry,capnometry, plethysmography (perfusion), or other physiologic variablesmay be used.

In steps 520 through 550, the QTdSI as a function of the continuous ordiscrete QT interval timeseries is calculated. In some embodiments, anyectopic beats and the sinus beats immediately preceding and followingthe ectopic beats are first eliminated, as part of a step 520 beforecalculating the maximal value of root-mean-square differences forisochronic points of the repolarization interval between pairs ofconsecutive beats. Low-pass filtering may be performed to removebaseline drift from the electrical signal, in some embodiments.Normalizing the maximal value of root-mean-square differences to theabsolute magnitude of the signal-averaged QRS complex may also beperformed, in some embodiments, before calculating and updating theQTdSI(t) timeseries. FIG. 6 depicts an illustrative example of the QRScomplex. Instructions carried on a computer-readable storage medium(e.g., for identifying QT intervals and calculating QTdSI(t)) can beimplemented in a high level procedural or object oriented programminglanguage to communicate with a computer system, in one embodiment.Alternatively in another embodiment, such instructions can beimplemented in assembly or machine language. The language further can becompiled or interpreted language, in one embodiment.

It is further contemplated that in some embodiments, the QTdSI-relatedprocessing occurring in steps 520 through 540 occurs in realtime or nearrealtime, simultaneously, as user-information is collected in step 510,thereby allowing a skilled operator to monitor an individual's QTdSIduring pharmacologic or exercise physiologic stress, if desired. Moregenerally, in some embodiments, processing steps 520 through 550 areperformed substantially simultaneously with the step 510 of collectingthe cardiac signals in near real-time, so as to enable the ambulatoryconsumer to go about their daily activities and receive smartphone orother mobile alert messages from brick 130 device in case anyelevated-risk conditions are detected.

At step 520, a corrected QT value is determined. This determination maybe performed by processing in sensor 116, in brick 130 or both. Inembodiments, artifact censoring and a noise filter or other DSPfiltering may be applied to the raw signal information. In someembodiments, the raw QT value is then normalized thereby resulting in acorrected QT value. In one embodiment the raw QT value is normalized towhat it would be if the heart rate was 60.

At steps 530, 540, and 550, QTdSI timeseries is determined, Lyapunovexponents are calculated, and used to determine stability of themonitored condition of the user. By way of example and not limitation,the methodology of the invention may be understood through the followingsteps: Let L(x₁, x₂, . . . , x_(n)) be a scalar function of n componentsof x, where the n components (sampled timepoints of the QT dispersiontimeseries QTd) comprise the vector x={x₁, . . . x_(n)}. L(x) ispositive-definite in a neighborhood N of the origin if L(x)>0 for allx≠0 in N and L(0)=0. Let x*(t)=0, t≧t₀ be the zero solution of thehomogeneous system x¢=Ax where x(0)=x₀=0. Then x*(t) is globally stablefor t≧t₀ if there exists L(x) with the following properties in someneighborhood N of 0: (i) L(x) and its partial derivatives arecontinuous; (ii) L(x) is positive-definite, or L(x)>0; and (iii)dL(x)/dt is negative-definite, or dL(x)/dt<0.

By (ii) the quadratic form L(x) exhibits an ellipsoid curve. By (iii),the ellipsoid curve shrinks to zero. Choose ε>0 such that Nε⊂N above.Any half-path starting in Nε remains in it because L(x) is a quadraticform (by (ii)) which exhibits an ellipsoid curve that is continuous aswell as its partial derivatives (by (i)). The same holds for everysufficiently small ε>0 and hence for every sufficiently smallneighborhood of the origin. The zero solution is therefore globallystable.

In other words, the system (dx/dt)=Ax is globally stable if and only iffor some positive-definite matrix W, the equation: A^(t) H+HA=−W has apositive-definite matrix H. If for some positive-definite matrix W, theequation A^(t) H+HA=−W has a positive-definite matrix H, let us showthat (dx/dt)=Ax is globally stable. Since H is positive-definite, thenL(x)=x^(t) Hx is positive-definite (where x^(t) is now the transpose ofx and not the time derivative), i.e. L(x)>0. Also, L(x)positive-definite implies that V(x) and its partial derivatives arecontinuous. Differentiating L(x), then: dL(x)/dt=(dx^(t)/dt)Hx+x^(t)H(dx/dt) or, as dx/dt=Ax: dL(x)/dt=(Ax)^(t) Hx+x^(t) HAx=x^(t) A^(t)Hx+x^(t) HAx=x^(t)(A^(t) H+HA) x. Thus, as A^(t) H+HA=−W:dL(x)/dt=x^(t)(−W)x. W determined to be positive-definite implies that−W is negative-definite, thus: dL(x)/dt=x^(t)(−W)x<0.

Finally, it is notable that (i) L(x) and its partial derivatives arecontinuous; (ii) V(x) is positive-definite; (iii) dL(x)/dt isnegative-definite. As a result, dx/dt is globally stable according toour previous theorem. Conversely, if dx/dt=Ax is stable, then for somepositive-definite matrix W, the equation A^(t) H+HA=−W has apositive-definite matrix H. dx/dt=Ax stable implies all the eigenvaluesof A are negative, i.e. λ<0 for any eigenvalue λ of A. Now, as λ x=Ax,then (Ax)^(t)=(λ x)^(t), which implies x^(t) A^(t)=λ x^(t). Thus,premultiplying A^(t) H+AH by x^(t) and post-multiplying it by x, thefollowing is obtained: x^(t)(A^(t) H+HA)x=x^(t)(−W)x; or: x^(t) A^(t)Hx+x^(t) HAx=x^(t)(−W)x; or substituting in λ x^(t) and λ x: λ x^(t)Hx+x^(t) Hλ x=x^(t)(−W)x; or simply: 2λ x^(t) Hx=x^(t)(−W)x. As −W isnegative-definite, then x^(t)(−W)x<0, thus 2λ x^(t) Hx<0. As λ<0 by theassumption of stability, then it must be that x^(t) Hx>0, or H is apositive-definite matrix. Accordingly, a real n×n matrix A is a stablematrix if and only if there exists a symmetric positive-definite matrixH such that A^(t) H+HA is negative-definite. In one embodiment, a choiceof W=1 may be made and H can be solved and solve for H in the equationA^(t) H+HA=−I. The solution has the form H=α(A^(t))⁻¹A⁻¹+βI where α andβ are constants. Thus, choosing a Lyapunov function, L(x)=x^(t) Hx, thissolution is used to determine H. The Lyapunov function or thread may beexecuted continuously, under a real-time operating system (RTOS), insome embodiments, enabling parameters and timeseries information to bepassed to the Lyapunov function or thread in near realtime.

Furthermore, in some embodiments, a second-order polynomial functionƒ(x)=r*x*(1−x) is utilized to represent a system whose stability may becharacterized by the invention. In one embodiment, the system may becharacterized by a function of different order or form. If the structureof a particular system is not known, the structure may be developed byTaylor series regression, spectral analysis or timeseries analysistechniques or other methods of modeling known to those of skill in theart.

At a step 530 a dispersion time series is calculated. In one embodiment,a standard deviation (SD) is calculated on an M-wide time series array,such as, for example, SD{QT₁, . . . , QT_(N−M)}, SD{QT₂, . . . ,QT_(N−M+1)}, . . . , SD{QT_(M+1), . . . , QT_(N)}. In one embodiment Mmay vary between 1000 to 10,000 samples; with accuracy generallyincreasing as the size of M increases.

At a step 540, Lyapunov exponents are calculated for each member of thetime series, thus: SD{QT₁, . . . , QT_(N−M)}→λ₁, SD{QT₂, . . . ,QT_(N−M+1)}→λ₂, . . . , SD{QT_(M+1), . . . , QT_(N)}→λ_(M+1). At a step550, stability is assessed based on the determined values of theLyapunov exponents. In some embodiments λ_(i)>0 implies an unstableprocess. In some embodiments, a threshold TH may be applied. Forexample, for instability to be present, λ_(i)>TH, which can account forminor fluctuations that may occur in the user, such as fluctuations thatmay arise when a user's activity level and heart rate change. In otherembodiments, such as in the example discussed later on, the differencebetween λ_(i) and a reference value is determined, and instability ispresent where this difference exceeds a certain threshold.

At a step 560 it is determined whether the user's QT interval is showingsigns of instability, based on the results of step 550. In oneembodiment, if the stability is present, then the process returns tostep 510 and additional ECG signals or other physiologic timeseriesinformation is obtained from one or more sensors 116. In one embodiment,new QT-interval information or other physiologic timeseries informationcontinuously collected as it is available simultaneously as processingfor determining stability-theoretic measures occurs. In one embodiment,the Lyapunov exponents are calculated on a sliding boxcar array that isM-samples wide, with new Lyapunov exponents calculated each W samples.In embodiments where W equals 1 heart beat, then new Lyapunov exponentsare calculated on the M-wide timeseries array for each new heart beat.In some embodiments, W may represent approximately 400 samples, thus forexample at 60 BPM, Lyapunov exponents would be calculated on the M-widearray every 400/60 minutes. If at step 560, the results of step 550indicate the presence of instability, then the method proceeds to step570. At a step 570, a user, health care provider, or decision supportsystem is notified that the user is becoming unstable. In oneembodiment, this instability indicates that the user is facing anincreased likelihood of SCD or other cardiac abnormality. In oneembodiment, this instability indicates a change in the patient'scondition, which may be for the better or worse. In one embodiment, theuser may be notified via brick 130 in the form of a text message,audible alarm or vibration. In one embodiment, the health care providermaybe notified via brick 130 in the form of a text message, call, orother appropriate form of communication. In one embodiment, a visual orgraphical display of the electrical signals or a numerical or digitizedrepresentation of the monitored ECG variables and stability indices maybe presented on brick 130, a user's computer communicatively coupled tobrick 130, or a health care provider's computer communicatively coupledto backend 190. For example, in one embodiment, an audible alert soundsor a vibration is emitted upon detection of patterns and QTdSI valuesindicative of actionable increased risk of SCD. In one embodiment, aradiofrequency message may be emitted tosecurity-/confidentiality-controlled, mated transceivers such asBlueTooth smartphones, Wi-Fi connections with personal computers orelectronic medical records systems, and similar devices.

Turning to FIG. 3 a flow diagram 300 is provided illustrating anexemplary method according to one embodiment. At a high level, flowdiagram 300 illustratively depicts a method for determining a QTdispersion stability index (QTdSI) for an individual. The QTdSI isdetermined by applying an objective function to user-derived informationsuch as ECG signal information obtained from one or more sensors 116.The method also includes determining the difference between thestability index value and a reference value to detect presence ofinstability of QT interval dispersion or other measurements. It has beendetermined, as further described below in connection to that asignificant difference between the two values indicates an increasedrisk of Sudden Cardiac Death (SCD) for an individual. In one embodiment,the reference value is selected based on other parameters associatedwith the user.

At a step 310, electrocardiogram (ECG) signals of a user are obtainedusing one or more sensors 116. User-information representative of theECG signals is communicated from one or more sensors 116 to brick 130.In some embodiments, pre-processing and conditioning of the ECG signalinformation, which may include, for example, artifact censoring,normalization, or DSP filtering, as described in connection with step520 in FIG. 5, takes place either at the sensor 116 in brick 130, orboth. At a step 320, QTdSI is determined in accordance with the methoddescribed in connection to steps 520 to 550 of FIG. 5. At a step 350,the difference between the QT dispersion stability index and a referencevalue is determined. Based on the results of this difference, at a step360, a determination is made as to whether the difference issignificant.

In one embodiment, significance is based on parameters associated withthe particular user. For example, a younger more active user without aknown condition may be afforded a greater difference than a user who hasa known history of cardiac arrhythmias or otherwise has a higher riskfor SCD. At step 360, where the determined difference is notsignificant, the method returns to step 310. In one embodiment, newQT-interval information or other physiologic timeseries information iscontinuously collected as it is available simultaneously as processingfor determining stability-theoretic measures occurs, as described abovein connection to FIG. 5. At step 360, where the determined difference issignificant, the method proceeds to a step 370. At step 370,notification of increased risk for SCD is provided. In one embodiment,the notification is provided in a manner as described at step 570 inconnection to FIG. 5.

Turning now to FIG. 4, a flow diagram 400 is provided illustrating anexemplary method according to one embodiment. At a high level, flowdiagram 400 illustratively depicts a method for determining a QTdispersion stability index (QTdSI) for an individual. With reference toFIG. 6, at a step 415 of FIG. 4, Q, R, S, and T wave morphology isdetermined for isochronic points between substantially-consecutive Twaves in signals representative of electrical activity of the heart ofthe individual. At a step 420 the QTdSI is computed, in accordance withthe method described in connection to steps 520 to 550 of FIG. 5, as afunction of the morphology determined in step 415. In the embodimentshown in FIG. 4, at a step 422, the user-derived signal is prepared. Insome embodiments, this includes pre-processing and conditioning of theECG signal information, which may include, for example, artifactcensoring, normalization, or DSP filtering, as described in connectionwith step 520 in FIG. 5. Such preprocessing may be performed by sensor116, brick 130, or both, in some embodiments. At a step 424, the maximalvalue of root-mean-square differences for isochronic points of arepolarization interval between pairs of substantially-consecutive Twaves is determined. At a step 426, the maximal value ofroot-mean-square differences are normalized to the absolute magnitude ofthe signal-averaged QRS complex. At a step 428, the Lyapunov exponentsare determined, in accordance with the method described above inconnection to FIG. 5.

FIG. 6 depicts an exemplary EKG signal of normal morphology identifyingthe P, Q, R, S, and T segments of the waveform. Where other heartbeats'EKG signals might be slightly faster, the waveform morphology may becompressed into a shorter interval of time. Other heartbeats' EKGsignals might also be slower, or certain segments of the waveform mightbe slightly different shape or length, for example a longer T-wavesegment corresponds to slower repolarization. The QT intervalcorresponds to the time-interval between where the Q-wave begins andwhere the T-wave ends for each beat.

By way of example using the embodiment of FIG. 1B, fifteen subjects witha history of ventricular arrhythmia and 13 control subjects with noknown risk factors for SCD were studied. There were 10 SCD events inthis cohort. Two subjects had suffered confirmed out-of-hospital cardiacarrest (OHCA), including one who had been implanted previously with anICD. Three additional long QT syndrome (LQTS) subjects were deemed highclinical risk on the basis of a personal history of syncope and a familyhistory of at least one sudden unexplained death. The control subjectswere free of known cardiovascular disease except for mild hypertensionin one subject.

Using the embodiment of FIG. 1B, the QTdSI accurately predictedventricular arrhythmias and SCD as shown in Table 1 below, where P<0.005Fisher Exact Test, two-tailed.

TABLE 1 SCD No SCD QTdSI positive 8 3 QTdSI negative 2 15

In this initial example, the sensitivity of the QTdSI metric to predictSCD was 80% and the specificity was 83%. The odds-ratio was 20 and thenumber-needed-to-treat (NNT) was 2.

Additionally, a small sample size of cases and controls was available,so risk stratification by ejection fraction or other patient-groupingvariables was not evaluated, here. In that regard, it is important toidentify those patients at high risk for SCD but who do not havesymptomatic left ventricular dysfunction. Secondly, some of the patientsin the initial study who had dilative cardiomyopathy had comorbid atrialfibrillation, frequent ectopic beats, or had a paced rhythm and so hadto be excluded from analysis. In follow-on studies, it is anticipatedthat specific submodels to predict SCD in the presence of thosecovariables will be developed. Thirdly, it should be noted that SCD isnot always because of ventricular arrhythmias. For example,bradyarrhythmias and electromechanical dissociation may be a morefrequent cause of SCD in nonischemic cardiomyopathies. It is thusanticipated that stability-theoretic prediction models as set forth inthe present invention should be useful in these circumstances. Fourthly,the cases and controls available to us were from acute-caremedical-surgical hospital-based settings.

Many different arrangements of the various components depicted, as wellas components not shown, are possible without departing from the spiritand scope of the present invention. Embodiments of the present inventionhave been described with the intent to be illustrative rather thanrestrictive. Alternative embodiments will become apparent to thoseskilled in the art that do not depart from its scope. A skilled artisanmay develop alternative means of implementing the aforementionedimprovements without departing from the scope of the present invention.

It will be understood that certain features and subcombinations are ofutility and may be employed without reference to other features andsubcombinations and are contemplated within the scope of the claims. Notall steps listed in the various figures need be carried out in thespecific order described. Accordingly, the scope of the invention isintended to be limited only by the following claims.

What is claimed is:
 1. One or more computer-readable storage deviceshaving computer-executable instructions embodied thereon that whenexecuted, facilitate a method for automatically providing a notificationfor ventricular arrhythmias in an individual that are likely to resultin sudden cardiac death (SCD), the method comprising: obtaining, fromone or more sensors, signals representative of electrical activity ofthe heart of said individual; determining, utilizing an objectivefunction, a QT dispersion stability index (QTdSI) from said signals, theobjective function comprising a timeseries calculated fromserially-acquired waveform data; the determining comprising calculatinga moving average of Lyapunov exponents; determining a difference betweenthe determined QTdSI and a reference value to detect the presence ofinstability of QT interval dispersion or other measurements in saidsignals; and providing a notification when an increased risk for SCD isdetermined; a significant difference being indicative of an increasedrisk of said individual of sudden cardiac death (SCD); the results ofthe objective function being used by a decision-support algorithm todetermine a quantitative risk for SCD; and the decision-supportalgorithm comprising at least one of a support vector machine, logisticregression equation, or a neural network.
 2. The computer-readablestorage devices of claim 1, wherein the objective function evaluatesdigitized electrocardiographic waveforms from one or a plurality ofprevious time intervals to classify a likelihood of a cascade of eventsleading to SCD within a future time interval.
 3. The computer-readablestorage devices of claim 1, wherein the Lyapunov exponent is evaluatedon one or a plurality of ECG or other physiologic variables as functionsof time.
 4. The computer-readable storage devices of claim 1, whereinthe decision-support algorithm comprises a support vector machineutilizing timeseries of calculated ECG variables including: width ofroot-mean-square (RMS) T-wave, total cosine T-wave dispersion, T-waveloop dispersion, normalized T-wave loop area, and relative T-waveresiduum.
 5. The computer-readable storage devices of claim 1, whereinthe decision-support algorithm comprises a combination of two or more ofa Lyapunov-based algorithm, a decision tree algorithm, or a supportvector machine.
 6. The computer-readable storage devices of claim 1,wherein the QTdSI is determined as a function of a continuous QTinterval timeseries.
 7. The computer-readable storage devices of claim1, wherein the QTdSI is determined as a function of a discrete QTinterval timeseries.
 8. The computer-readable storage devices of claim1, wherein the Lyapunov exponent is calculated for each new heart beat.9. The computer-readable storage devices of claim 1, wherein thetimeseries comprises approximately 400 samples.
 10. Thecomputer-readable storage devices of claim 1, wherein the objectivefunction comprises a second-order polynomial function determined usingTaylor series regression or spectral analysis.
 11. A method forautomatically providing a notification for ventricular arrhythmias in anindividual that are likely to result in sudden cardiac death (SCD), themethod comprising: obtaining, from one or more sensors, ECG signalsrepresentative of electrical activity of the heart of said individual;and determining, utilizing an objective function, a QT dispersionstability index (QTdSI) from said signals, the objective functioncomprising a timeseries calculated from serially-acquired waveform dataas functions of time the determining comprising calculating a movingaverage of Lyapunov exponents of a plurality of ECG or other physiologicvariables; determining a difference between the determined QTdSI and areference value to detect the presence of instability of QT intervaldispersion or other measurements in said signals; a significantdifference being indicative of an increased risk of said individual ofSCD; and providing a notification to a health care provider when saidincreased risk for SCD is indicated; the results of the objectivefunction being used by a decision-support algorithm to determine aquantitative risk for SCD; and the decision-support algorithm comprisingat least one of a support vector machine, logistic regression equation,or a neural network.
 12. The method of claim 11, wherein the objectivefunction evaluates digitized electrocardiographic waveforms from one ora plurality of previous time intervals to classify a likelihood of acascade of events leading to SCD within a future time interval.
 13. Themethod of claim 11, wherein the decision-support algorithm comprises asupport vector machine utilizing timeseries of calculated ECG variablesincluding: width of root-mean-square (RMS) T-wave, total cosine T-wavedispersion, T-wave loop dispersion, normalized T-wave loop area, andrelative T-wave residuum.
 14. The method of claim 11, wherein thedecision-support algorithm comprises a combination of two or more of aLyapunov-based algorithm, a decision tree algorithm, or a support vectormachine.
 15. The method of claim 11, wherein the QTdSI is determined asa function of a continuous QT interval timeseries.
 16. The method ofclaim 11, wherein the QTdSI is determined as a function of a discrete QTinterval timeseries.